Abtin Zandi 
FastAPI came onto the Python scene in late 2018, created by Sebastián Ramírez to solve a growing need in the developer community for a framework that combined speed, clarity, and modern features like automatic API documentation. Built on top of Starlette for web handling and Pydantic for data validation, FastAPI quickly captured attention because it leveraged Python’s type hints to eliminate boilerplate, validate data cleanly, and generate interactive OpenAPI documentation automatically — all without sacrificing performance! These design choices set it apart from older frameworks by making APIs simpler to write & easier to maintain.
Behind the scenes, FastAPI also embraced asynchronous programming from day one, giving it a performance edge for I/O-bound workloads like database queries or external API calls (the kind you see in AI inference or MCP servers). As more people built microservices, mobile backends, and high-traffic APIs, specially with high intrest in AI, FastAPI’s reputation grew alongside its adoption, making it one of the most starred and loved Python frameworks in recent years.
FastAPI, AI, and Speed
In the AI ecosystem, FastAPI features like async programming are game changers! When deploying LLM-powered services, you often have backend layers that accept prompts, hit vector databases for retrieval, and call model inference endpoints. FastAPI’s async roots mean these I/O-heavy sequences — fetching embeddings, querying search indices, awaiting model responses — can be managed concurrently without locking up a single thread. For RAG (retrieval-augmented generation) pipelines, where you couple retrieval with generation, FastAPI lets developers build lightweight microservices that route requests efficiently and scale horizontally. It integrates cleanly with frameworks like LangChain, helping you expose endpoints that serve embeddings, agent instructions, or chained LLM prompts, all within the same API stack. Its docs are generated by default, so complex AI workflows become easier to onboard and maintain.
What is Pydentic? What is it’s usage inside FastAPI?
Pydantic is a widely used Python library for data validation and settings management that leverages Python’s type annotations to automatically check and enforce that data conforms to the types and structure you define in your code, turning type hints into runtime validation rules and clean serialization/deserialization logic.
FastAPI makes heavy use of Pydantic models to handle data validation, serialization, and the generation of API documentation automatically. By defining your request and response data shapes using standard Python type hints in Pydantic classes, FastAPI can validate incoming data, convert it to the right types, and include those models in the generated OpenAPI schema, which powers the interactive docs you see in tools like Swagger UI and ReDoc — all without writing extra boilerplate.
Here we have an example of the pydantic usage inside FastAPI
from fastapi import FastAPI, HTTPException
from uvicorn import run
from pydantic import BaseModel
app = FastAPI()
class NEWS(BaseModel):
title: str
body: str
author: str = "Abtin Zandi"
is_visible: bool = True
@app.post("/news/")
def create_news(news: NEWS):
'''Endpoint to create a news item.
Args:
news (NEWS): The news item to create.
Returns:
dict: A confirmation message with the news details.
'''
#In real application, you would save the news to a database here!
#Here we just return the news data as a confirmation.
return {
"message": "News received",
"news_title": news.title,
"news_body": news.body,
"news_author": news.author,
"is_it_visible": news.is_visible
}
if __name__ == "__main__":
run(app, host="0.0.0.0", port=8000)if we launch this simple News server on our local machine, after calling provided curl inside terminal(Also we can use api agents like Postman here) we will recieve a simple confirmaiton message that our passed data is processed succefully.
curl -X POST "http://127.0.0.1:8000/news/" -H "Content-Type: application/json" -d '{"title":"FastAPI Tip","body":"Use Pydantic models for input validation.","author":"Abnzandi","is_visible":true}'
Now lets pass a wrong data request like this curl command below where we have not provided title and is_visible fields correctly:
curl -X POST "http://127.0.0.1:8000/news/" -H "Content-Type: application/json" -d '{"body":"Use Pydantic models for input validation.","author":"Abnzandi","wrong_param":true}'As you can see in the provided image below, which is our response from the /news/ endpoint, Pydantic has issued an error related to missing important data — specifically the title in this scenario! Despite the fact that we have not provided the is_visible field, since we declared a predefined value (true), we won’t have any problem. However, title is missing, and Pydantic handled it at the receiving time.
A Quick Look at Django and Flask
Django and Flask are two of Python’s other most familiar web frameworks. Flask, born in 2010, is a minimalist microframework: it gives you a web server and routing tools, and you add extensions for the features you want. It’s synchronous by default and perfect for quick prototypes or simple apps. the mian idea behind Flask at that time was simplicity of its developement and learning which makes it very suitable for students projects, light and simple projects, or testing a functionality wihtout being forced to follow restrict rules of other frameworks.
On the other side, Django goes in the opposite direction as a full-stack framework: it includes an ORM (object-relational mapper), admin dashboards, authentication systems, templating engines, and a bunch of conventions that make building traditional web apps easier and more secure right out of the box.
Beyond its built-in features, Django follows a clear Model-View-Controler(MVC) architectural pattern that helps developers organize and separate concerns within an application. This separation makes codebases easier to maintain and scale, especially in larger projects, and ensures that each layer has a distinct responsibility in the request-response cycle. Django also supports both WSGI (Web Server Gateway Interface) for traditional synchronous request handling and ASGI (Asynchronous Server Gateway Interface) for asynchronous communication like WebSockets and long-lived connections, giving you flexibility to build both classic web apps and modern real-time features without changing frameworks.
To be Fast, Simple, or Complete, This is the Question!
FastAPI, Django, and Flask each speak a slightly different language. FastAPI is optimized for API-first development with native async support that makes it ideal for high-concurrency and fast microservices. Its type-driven design and automatic documentation appeal strongly to developers building scalable backend APIs or serving machine learning inferences. Django, with its batteries-included philosophy, shines when you need a robust, secure, full-featured web application with built-in ORM, templating, and admin tooling — it’s often the best choice for traditional, large-scale apps where you want most features available without stitching them together manually.
Flask sits somewhere between these philosophies: it’s incredibly flexible and easy to pick up, letting you build lightweight services or extend it with plugins as your app grows. But this freedom comes with responsibilities: you choose your tools for things like validation, async support, or documentation. Because Flask lacks native async and automatic docs, it can lag behind FastAPI when building performant API layers for AI and modern web services. On the other hand, Flask’s simplicity is perfect for rapid prototypes or internal tools where performance isn’t the bottleneck.
Final Word
FastAPI isn’t just another Python framework — it’s a reflection of where web services are heading: asynchronous by default, type-aware, and built for performance. Whether you’re exposing a simple REST endpoint, building a microservice, or powering an AI pipeline where speed and scalability matter, FastAPI gives you the tools to do it cleanly and efficiently. Django and Flask remain powerful in their own domains — Django for full-stack web applications, and Flask for quick, flexible developement of the new services — but it’s FastAPI’s embrace of modern Python features that’s helping it rise to the forefront of API and AI infrastructure in 2025.
Resources and Useful links:
https://en.wikipedia.org/wiki/FastAPI
https://fastapi.tiangolo.com/python-types
https://fastapi.tiangolo.com/reference/openapi/docs
https://blog.jetbrains.com/pycharm/2025/02/django-flask-fastapi/
https://blog.jetbrains.com/pycharm/2025/02/django-flask-fastapi/
